In the present work, we developed a machine learning potential for
pure aluminium by means of a high dimensional neural network on the
basis of the Behler-Parrinello approach [1]}, [2]}.
The ML potential is devoted to the description of condensed phases,
namely liquid and solid states at ambient pressure as well as those
at pressures up to 300 GPa with resulting temperatures as high
as 8000 K. A crucial point was the training of the potential with
a data set generated by DFT-based simulations not only to cover the
targeted domain of thermodynamic states for a question of transferability
but also to consider for each of them in a physical meaningful
manner the time fluctuations by an appropriate sampling of phase
space trajectories obtained by ab initio molecular dynamics.
This allows to include in the training the relevant accessible
microstates of the considered thermodynamic states. Another approach
based on metadynamics was shown to be efficient in selecting the
relevant configurations to train the neural network [3]}.
